Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.640628 |
nan |
| auc |
0.914552 |
nan |
| f1 |
0.911392 |
0.514925 |
| accuracy |
0.934579 |
0.514925 |
| precision |
0.923077 |
0.514925 |
| recall |
0.975 |
0 |
| mcc |
0.859727 |
0.514925 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.640628 |
nan |
| auc |
0.914552 |
nan |
| f1 |
0.911392 |
0.514925 |
| accuracy |
0.934579 |
0.514925 |
| precision |
0.923077 |
0.514925 |
| recall |
0.9 |
0.514925 |
| mcc |
0.859727 |
0.514925 |
Confusion matrix (at threshold=0.514925)
|
Predicted as B |
Predicted as M |
| Labeled as B |
64 |
3 |
| Labeled as M |
4 |
36 |
Learning curves

Decision Tree
Tree #1
Rules
if (radius_worst <= 16.805) and (concave points_worst <= 0.152) and (perimeter_se <= 6.54) then class: B (proba: 97.01%) | based on 201 samples
if (radius_worst > 16.805) and (concavity_worst > 0.218) and (concavity_se > 0.018) then class: M (proba: 100.0%) | based on 98 samples
if (radius_worst <= 16.805) and (concave points_worst > 0.152) and (radius_mean <= 14.43) then class: M (proba: 100.0%) | based on 10 samples
if (radius_worst > 16.805) and (concavity_worst > 0.218) and (concavity_se <= 0.018) then class: M (proba: 66.67%) | based on 3 samples
if (radius_worst > 16.805) and (concavity_worst <= 0.218) and (radius_worst <= 18.705) then class: B (proba: 100.0%) | based on 3 samples
if (radius_worst > 16.805) and (concavity_worst <= 0.218) and (radius_worst > 18.705) then class: M (proba: 100.0%) | based on 2 samples
if (radius_worst <= 16.805) and (concave points_worst > 0.152) and (radius_mean > 14.43) then class: B (proba: 100.0%) | based on 1 samples
if (radius_worst <= 16.805) and (concave points_worst <= 0.152) and (perimeter_se > 6.54) then class: M (proba: 100.0%) | based on 1 samples
Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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